The document discusses sharing slides from a lecture on Gaussian Bayes classifiers. It notes that the original slides are available and encourages others to use and modify the slides for their own teaching needs. Users are asked to include attribution to the original source if using a significant portion of the slides.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Prediction of heart disease using machine learning.pptxkumari36
1. The document discusses using machine learning techniques to predict heart disease by evaluating large datasets to identify patterns that can help predict, prevent, and manage conditions like heart attacks.
2. It proposes using data analytics based on support vector machines and genetic algorithms to diagnose heart disease, claiming genetic algorithms provide the best optimized prediction models.
3. The key modules described are uploading training data, pre-processing the heart disease data, using machine learning to predict heart disease, and generating graphical representations of the analyses.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
The document discusses different types of data models and their evolution. It describes hierarchical, network, relational, entity relationship, and object oriented models. Each new model aimed to improve on limitations of previous approaches. The models can be classified at different levels of abstraction, from external views specific to business units to conceptual and internal representations within the database.
Pandas is a powerful Python library for data analysis and manipulation. It provides rich data structures for working with structured and time series data easily. Pandas allows for data cleaning, analysis, modeling, and visualization. It builds on NumPy and provides data frames for working with tabular data similarly to R's data frames, as well as time series functionality and tools for plotting, merging, grouping, and handling missing data.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Prediction of heart disease using machine learning.pptxkumari36
1. The document discusses using machine learning techniques to predict heart disease by evaluating large datasets to identify patterns that can help predict, prevent, and manage conditions like heart attacks.
2. It proposes using data analytics based on support vector machines and genetic algorithms to diagnose heart disease, claiming genetic algorithms provide the best optimized prediction models.
3. The key modules described are uploading training data, pre-processing the heart disease data, using machine learning to predict heart disease, and generating graphical representations of the analyses.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
The document discusses different types of data models and their evolution. It describes hierarchical, network, relational, entity relationship, and object oriented models. Each new model aimed to improve on limitations of previous approaches. The models can be classified at different levels of abstraction, from external views specific to business units to conceptual and internal representations within the database.
Pandas is a powerful Python library for data analysis and manipulation. It provides rich data structures for working with structured and time series data easily. Pandas allows for data cleaning, analysis, modeling, and visualization. It builds on NumPy and provides data frames for working with tabular data similarly to R's data frames, as well as time series functionality and tools for plotting, merging, grouping, and handling missing data.
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
This document discusses Naive Bayes classifiers. It begins with an overview of probabilistic classification and the Naive Bayes approach. The Naive Bayes classifier makes a strong independence assumption that features are conditionally independent given the class. It then presents the algorithm for Naive Bayes classification with discrete and continuous features. An example of classifying whether to play tennis is used to illustrate the learning and classification phases. The document concludes with a discussion of some relevant issues and a high-level summary of Naive Bayes.
This document summarizes the Iris flower data set, which contains measurements of 150 iris flowers from three species. It describes the four attributes measured (sepal length and width, petal length and width) and explains that one species is linearly separable from the other two. Various visualizations and analyses are proposed to better understand the relationships between attributes and species, including box plots, correlation matrices, and evaluating classification algorithms using different feature combinations. Accuracy results are presented for models trained and tested on the data split in various ways.
This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.
This document provides an overview of indexing and hashing techniques for database systems. It discusses ordered indices like B-trees which store index entries in sorted order, and hash indices which distribute entries uniformly using a hash function. The key topics covered are basic indexing concepts, ordered indices, B-tree index files, hashing techniques, performance metrics for evaluating indices, and updating indices for insertions and deletions. B-tree indices are highlighted as an efficient structure that automatically reorganizes with updates while avoiding the need to periodically reorganize entire files like indexed sequential files.
Cloud computing provides a way for organizations to share distributed resources over a network. However, data security is a major concern in cloud computing since data is stored remotely. The document discusses several techniques used for data security in cloud computing including authentication, encryption, data masking, and data traceability. The latest technologies discussed are a cloud information gateway that can control data transmission and secure logic migration that transfers applications to an internal sandbox for secure execution.
The document discusses using web 2.0 tools for collaboration in the cloud. It defines collaboration 2.0 as adding distributed computing and collaboration platforms that allow for distance and asynchronicity. Benefits include social networks functioning as professional networks and blending synchronous and asynchronous work. Various categories of tools are covered, including social calendars, networking sites, bookmarking, desktops, wikis and documents. Examples like Google Docs, Dropbox and PBWorks are provided. The document advocates using these tools for projects, communication, organizing information and backups.
Review Paper on Implementation Technology to Repair Pothole Using Waste PlasticIRJET Journal
The document describes a system for repairing potholes using waste plastic. Potholes form due to factors like water absorption in cracks and heavy vehicle traffic. The system would detect potholes using ultrasonic sensors, melt shredded waste plastic using induction heating, and pour the molten plastic into potholes. Using plastic to fill potholes could extend road lifespan from 3 to over 5 years. It addresses both the problems of pothole formation and plastic waste accumulation, providing a cheaper and longer-lasting alternative to traditional pothole repair methods.
This document discusses k-nearest neighbor (k-NN) machine learning algorithms. It explains that k-NN is an instance-based, lazy learning method that stores all training data and classifies new examples based on their similarity to stored examples. The key steps are: (1) calculate the distance between a new example and all stored examples, (2) find the k nearest neighbors, (3) assign the new example the most common class of its k nearest neighbors. Important considerations include the distance metric, value of k, and voting scheme for classification.
Weka is a collection of machine learning algorithms and data pre-processing tools developed at the University of Waikato. It contains tools for data pre-processing, classification, regression, clustering, association rule mining, and visualization. Weka is open source, free to use, and popular for research and applications. It has a graphical user interface and supports a variety of data formats including ARFF files.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
This document discusses approximate inference in Bayesian networks using sampling methods. It introduces random number generation, which is important for sampling algorithms. Random number generators in programming languages typically generate uniform random numbers, but different distributions are needed for sampling Bayesian networks. The document covers generating random numbers from univariate and multivariate distributions to estimate probabilities for approximate inference in Bayesian networks.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The KDD process involves several steps: data cleaning to remove noise, data integration of multiple sources, data selection of relevant data, data transformation into appropriate forms for mining, applying data mining techniques to extract patterns, evaluating patterns for interestingness, and representing mined knowledge visually. The KDD process aims to discover useful knowledge from various data types including databases, data warehouses, transactional data, time series, sequences, streams, spatial, multimedia, graphs, engineering designs, and web data.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Heart disease prediction using machine learning algorithm Kedar Damkondwar
The document summarizes a seminar presentation on predicting heart disease using machine learning algorithms. It introduces the problem of heart disease prediction and the motivation to develop an automated system to assist in diagnosis and treatment. It reviews several existing studies that used methods like decision trees, naive Bayes, neural networks, and support vector machines to predict heart disease risk factors. The objectives of the presented model are to develop a predictive system using machine learning techniques to analyze heart data and help reduce medical costs and human biases. The proposed model and applications in medical institutions and hospitals are also discussed.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
This document provides an executive summary and introduction to Bayes networks. It contains 9 slides that describe Bayes networks at a high level, provide simple illustrative examples, and discuss how Bayes networks can be built from expert knowledge or data. Real-world examples of Bayes networks in applications such as medical diagnosis, manufacturing systems, and information retrieval are also briefly mentioned.
The document discusses the need for balance between public and private housing in Niagara Falls. While public housing is necessary to provide affordable options given the low median income, concentrating only public housing can depress areas and reduce the city's tax base. The city struggles with balancing this need for public housing with generating tax revenue, as subsidized housing does not contribute to taxes and abandoned buildings and neighborhoods also provide little tax income.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
This document discusses Naive Bayes classifiers. It begins with an overview of probabilistic classification and the Naive Bayes approach. The Naive Bayes classifier makes a strong independence assumption that features are conditionally independent given the class. It then presents the algorithm for Naive Bayes classification with discrete and continuous features. An example of classifying whether to play tennis is used to illustrate the learning and classification phases. The document concludes with a discussion of some relevant issues and a high-level summary of Naive Bayes.
This document summarizes the Iris flower data set, which contains measurements of 150 iris flowers from three species. It describes the four attributes measured (sepal length and width, petal length and width) and explains that one species is linearly separable from the other two. Various visualizations and analyses are proposed to better understand the relationships between attributes and species, including box plots, correlation matrices, and evaluating classification algorithms using different feature combinations. Accuracy results are presented for models trained and tested on the data split in various ways.
This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.
This document provides an overview of indexing and hashing techniques for database systems. It discusses ordered indices like B-trees which store index entries in sorted order, and hash indices which distribute entries uniformly using a hash function. The key topics covered are basic indexing concepts, ordered indices, B-tree index files, hashing techniques, performance metrics for evaluating indices, and updating indices for insertions and deletions. B-tree indices are highlighted as an efficient structure that automatically reorganizes with updates while avoiding the need to periodically reorganize entire files like indexed sequential files.
Cloud computing provides a way for organizations to share distributed resources over a network. However, data security is a major concern in cloud computing since data is stored remotely. The document discusses several techniques used for data security in cloud computing including authentication, encryption, data masking, and data traceability. The latest technologies discussed are a cloud information gateway that can control data transmission and secure logic migration that transfers applications to an internal sandbox for secure execution.
The document discusses using web 2.0 tools for collaboration in the cloud. It defines collaboration 2.0 as adding distributed computing and collaboration platforms that allow for distance and asynchronicity. Benefits include social networks functioning as professional networks and blending synchronous and asynchronous work. Various categories of tools are covered, including social calendars, networking sites, bookmarking, desktops, wikis and documents. Examples like Google Docs, Dropbox and PBWorks are provided. The document advocates using these tools for projects, communication, organizing information and backups.
Review Paper on Implementation Technology to Repair Pothole Using Waste PlasticIRJET Journal
The document describes a system for repairing potholes using waste plastic. Potholes form due to factors like water absorption in cracks and heavy vehicle traffic. The system would detect potholes using ultrasonic sensors, melt shredded waste plastic using induction heating, and pour the molten plastic into potholes. Using plastic to fill potholes could extend road lifespan from 3 to over 5 years. It addresses both the problems of pothole formation and plastic waste accumulation, providing a cheaper and longer-lasting alternative to traditional pothole repair methods.
This document discusses k-nearest neighbor (k-NN) machine learning algorithms. It explains that k-NN is an instance-based, lazy learning method that stores all training data and classifies new examples based on their similarity to stored examples. The key steps are: (1) calculate the distance between a new example and all stored examples, (2) find the k nearest neighbors, (3) assign the new example the most common class of its k nearest neighbors. Important considerations include the distance metric, value of k, and voting scheme for classification.
Weka is a collection of machine learning algorithms and data pre-processing tools developed at the University of Waikato. It contains tools for data pre-processing, classification, regression, clustering, association rule mining, and visualization. Weka is open source, free to use, and popular for research and applications. It has a graphical user interface and supports a variety of data formats including ARFF files.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
This document discusses dimensionality reduction techniques for data mining. It begins with an introduction to dimensionality reduction and reasons for using it. These include dealing with high-dimensional data issues like the curse of dimensionality. It then covers major dimensionality reduction techniques of feature selection and feature extraction. Feature selection techniques discussed include search strategies, feature ranking, and evaluation measures. Feature extraction maps data to a lower-dimensional space. The document outlines applications of dimensionality reduction like text mining and gene expression analysis. It concludes with trends in the field.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
This document discusses approximate inference in Bayesian networks using sampling methods. It introduces random number generation, which is important for sampling algorithms. Random number generators in programming languages typically generate uniform random numbers, but different distributions are needed for sampling Bayesian networks. The document covers generating random numbers from univariate and multivariate distributions to estimate probabilities for approximate inference in Bayesian networks.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The KDD process involves several steps: data cleaning to remove noise, data integration of multiple sources, data selection of relevant data, data transformation into appropriate forms for mining, applying data mining techniques to extract patterns, evaluating patterns for interestingness, and representing mined knowledge visually. The KDD process aims to discover useful knowledge from various data types including databases, data warehouses, transactional data, time series, sequences, streams, spatial, multimedia, graphs, engineering designs, and web data.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Heart disease prediction using machine learning algorithm Kedar Damkondwar
The document summarizes a seminar presentation on predicting heart disease using machine learning algorithms. It introduces the problem of heart disease prediction and the motivation to develop an automated system to assist in diagnosis and treatment. It reviews several existing studies that used methods like decision trees, naive Bayes, neural networks, and support vector machines to predict heart disease risk factors. The objectives of the presented model are to develop a predictive system using machine learning techniques to analyze heart data and help reduce medical costs and human biases. The proposed model and applications in medical institutions and hospitals are also discussed.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
This document provides an executive summary and introduction to Bayes networks. It contains 9 slides that describe Bayes networks at a high level, provide simple illustrative examples, and discuss how Bayes networks can be built from expert knowledge or data. Real-world examples of Bayes networks in applications such as medical diagnosis, manufacturing systems, and information retrieval are also briefly mentioned.
The document discusses the need for balance between public and private housing in Niagara Falls. While public housing is necessary to provide affordable options given the low median income, concentrating only public housing can depress areas and reduce the city's tax base. The city struggles with balancing this need for public housing with generating tax revenue, as subsidized housing does not contribute to taxes and abandoned buildings and neighborhoods also provide little tax income.
The document discusses support vector machines (SVMs) and how they find the maximum margin linear classifier to classify data. Specifically, it explains that SVMs:
1) Find the linear decision boundary that maximizes the margin or distance between the boundary and the closest data points of each class.
2) The maximum margin classifier is the simplest type of SVM called a linear SVM (LSVM).
3) The margin is computed in terms of the weights w and bias b that define the decision boundary. Maximizing this margin leads to the optimal separating hyperplane.
The document provides information about the SME Team at the International Digital Laboratory (IDL) including contacts and services available to help small and medium-sized enterprises (SMEs). It introduces several members of the SME Team, their backgrounds and areas of expertise. The team can help SMEs with opportunities to collaborate with researchers, access to facilities, networking and training events, and knowledge transfer partnerships.
Predicting Real-valued Outputs: An introduction to regressionguestfee8698
This document provides an introduction to regression analysis, which is used to predict real-valued outputs. It discusses single and multivariate linear regression, including how to calculate the maximum likelihood estimate of the regression coefficient(s). It also covers extensions such as adding a constant term, handling varying noise levels in the data, and nonlinear regression models. The goal is to estimate the parameters that best predict the output values given the input features.
An interim executive can provide several key benefits to organizations:
1. They can quickly fill important leadership openings like Chief Sales Officer or Chief Marketing Officer to ensure business continues uninterrupted and avoid delays from a prolonged search for a new permanent hire.
2. They can successfully complete special projects or strategic initiatives without the overhead costs of a permanent hire and are often available on short notice to help a company move quickly.
3. They allow companies to take the time needed to find the best permanent hire for a key position while still meeting that role's responsibilities in the interim.
Le app Windows universal consentono di sviluppare app per Windows Phone e Windwos 8 condividendo gli oggetti comuni. In questa presentazione introduttiva ne vedremo gli aspetti chiave.
- The document discusses how much people earn from their time based on hours worked and occupations. Some work 40 hours and earn under $40,000 while others work over 40 hours and earn over $100,000. A few work less than 40 hours and earn over $500,000.
- It introduces a multi-level marketing company called FHTM that allows people to build a business and earn residual income by gathering customers and introducing others to do the same. The compensation plan is described as very generous.
- Details are provided on products/services offered, business model, leadership bonuses, and a car program for high achievers. Building a team of 3 who each build a team of 3 is emphasized as the simple
The document discusses missed opportunities in Niagara Falls, NY across three areas: the Upper River Parkway, the Lower River Gorge, and Downtown. The Upper River Parkway separates historic homes from the river and has blocked commercial development for years. The Lower River Gorge, with spectacular views, has never reached its potential as a tourist attraction. Downtown has 90,000 square feet of empty retail space in the heart of the tourist district that could boost the city's tourism. Neglecting historic buildings is a lost opportunity to enhance visitors' experiences.
- The document discusses how well people are paid based on the hours they work and their job type, with some people earning over $100,000 working more than 40 hours per week while others earn less than $40,000 working 40 hours.
- It then contrasts employees, who make up 95% of the population but only 5% of total wealth, with business owners, who make up just 5% of the population but 95% of total wealth. Business owners' time is leveraged through business ownership while employees trade their time.
- The document is promoting a multi-level marketing company called FHTM, outlining its business model, compensation plan showing potential earnings at different levels, and products/services offered.
The Digital Lab provides knowledge services and technologies to small and medium sized businesses through their SME Lab program. This includes demonstrators of leading edge technologies, seminars on digital technologies, and analysis sessions to identify relevant solutions for businesses. They also offer strategy discussions, collaborative research projects, and access to research in areas like e-business, e-security, experiential engineering, and more. Experts in these areas describe challenges they are addressing in fields like securing data, trust management, high-integrity testing, and applying human factors research to product and environment design.
The document discusses the concept of PAC (Probably Approximately Correct) learning. It begins by describing a learning scenario where a hidden hypothesis is chosen by nature, and a learner tries to approximate this hypothesis based on randomly generated training data. It then defines what it means for a learned hypothesis to be "bad" or have high test error, and shows that by choosing a large enough random training set, the probability of learning a bad hypothesis can be bounded. Finally, it provides the formula for calculating the minimum size of the random training set needed to guarantee this probability bound.
The document discusses the Vapnik-Chervonenkis (VC) dimension, which is a measure of the "power" or capacity of a learning machine or classifier. The VC dimension allows one to estimate the error of a classifier on future data based only on its training error and VC dimension. Specifically, with high probability the test error is bounded above by the training error plus an additional term involving the VC dimension. The document also introduces the concept of a classifier "shattering" a set of points, which relates to calculating the VC dimension.
This document contains slides from a lecture on Hidden Markov Models given by Andrew W. Moore. The slides introduce Markov systems as having a set of states and discrete time steps, with the system occupying exactly one state at each time step chosen randomly based on the previous state. The slides provide examples of state transition probabilities in a Markov system and note that the Markov property means the next state depends only on the current state.
- The document discusses K-means clustering and hierarchical clustering.
- It provides an overview of the K-means clustering algorithm, including how it aims to optimize clustering by minimizing distortion and finding cluster centroids.
- The K-means algorithm involves assigning points to centroids, updating centroids to be the mean of each cluster, and repeating until convergence.
This document provides instructions for other teachers to use and modify slides from a lecture on clustering with Gaussian mixtures given by Andrew W. Moore. It notes that the PowerPoint originals are available and encourages comments and corrections. Users are asked to include attribution if using a significant portion of the slides.
A Short Intro to Naive Bayesian Classifiersguestfee8698
This document introduces Naive Bayes classifiers and their use in document classification. It begins with an overview of Naive Bayes theory and classifiers. Examples are then provided to illustrate how to estimate probabilities for the classifier from sample training data and how to perform classification of new documents. The assumptions and advantages of the Naive Bayes approach are discussed. In particular, it notes that Naive Bayes classifiers can be efficiently constructed, even with many attributes, and generally perform well despite their "naivety".
This document contains slides from a lecture on Bayes net structure learning given by Andrew W. Moore. The slides introduce Bayes net structure learning as an additional machine learning method. They cover scoring Bayes net structures based on a Bayesian Information Criterion, and searching over possible structures to find the one with the best score. The purpose is to teach students about learning the structure of Bayesian networks from data.
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents.
- The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked".
- For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
This document discusses Bayes networks for representing and reasoning about uncertainty. It begins by noting the benefits of using joint distributions to describe uncertain worlds but also the problem of using joint distributions due to their complexity. Bayes networks allow building joint distributions in manageable chunks by representing conditional independence relationships between variables. The document then discusses representing uncertainty using probability and key concepts in probability such as conditional probability, Bayes' rule, and working through examples to demonstrate their application.
The document discusses various machine learning algorithms including polynomial regression, quadratic regression, radial basis functions, and robust regression. It provides mathematical formulas and visual examples to explain how each algorithm works. The key ideas are that polynomial regression fits nonlinear functions of inputs, quadratic regression extends linear regression by including quadratic terms, radial basis functions use kernel functions centered at data points to perform nonlinear regression, and robust regression aims to fit data robustly by down-weighting outliers.
Instance-based learning (aka Case-based or Memory-based or non-parametric)guestfee8698
This document provides an overview of instance-based learning techniques. It begins by introducing 1-nearest neighbor classification and regression, which makes predictions based on the single closest training example. It then discusses how k-nearest neighbor addresses some of the issues with 1-NN by considering the average output of the k closest examples. The document also covers kernel regression, which weights all training examples based on their distance from the query point. It demonstrates how varying the kernel width parameter and query point affects the predictions. Instance-based learning relies on storing past examples and making predictions by comparing new examples to similar stored examples.
1) The document discusses linear regression and how it can be used to model the relationship between input variables (x) and output variables (y).
2) Linear regression finds the best fitting linear relationship by minimizing the sum of squared errors between the actual y values and the predicted y values from the linear model.
3) The maximum likelihood estimate of the parameters for linear regression can be found in closed form as a function of the input and output data.
Cross-validation is a method for detecting and preventing overfitting in machine learning models. It involves randomly splitting a dataset into a training set and a test set. Models are trained on the training set and their performance is evaluated on the held-out test set, allowing models to be selected based on their expected generalization error rather than just their in-sample fit. The document describes using linear regression, quadratic regression, and nonparametric regression on simulated datasets to demonstrate how cross-validation can be used to select the model that will best predict future data.
The document discusses maximum likelihood estimation for learning parameters of univariate Gaussian distributions from data. It shows that the maximum likelihood estimates (MLEs) for the mean (μ) is simply the sample mean of the data. The MLE for the variance (σ2) is the unbiased sample variance of the data. Maximum likelihood estimation is a fundamental technique in statistical data analysis and learning Gaussian distributions lays the groundwork for more advanced methods.
This document is a set of slides about Gaussians and their use in data mining. It begins with an introduction explaining why Gaussians are important tools. It then covers the entropy of a probability density function, univariate and multivariate Gaussians, and how Gaussians are used with Bayes' rule and maximum likelihood estimation. The slides provide definitions, visual examples, and key properties of Gaussian distributions. The author encourages others to use and modify the slides for teaching purposes and requests attribution if significant portions are used.
This document provides an introduction to probabilistic and Bayesian analytics through a series of slides from a lecture by Andrew W. Moore. The key points covered include:
- Probability is used to represent uncertainty and is quantified by the fraction of possible worlds where an event occurs.
- The axioms of probability are introduced and interpreted visually, including that probabilities must be between 0 and 1 and the addition rule for mutually exclusive events.
- Important theorems are derived from the axioms, such as the probability of the complement of an event.
- Conditional probability is defined as the probability of one event given another using a visual representation.
- Bayes' rule for updating probabilities based on new information is
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.